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Is Managed Care Superior to Traditional Fee-For-Service among HIV-Infected Beneficiaries of Medicaid? David Zingmond, MD, PhD. UCLA Division of General Internal Medicine and Health Services Research June 8, 2004. Background (1). Medicaid is the largest payer of healthcare for HIV/AIDS

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slide1

Is Managed Care Superior to Traditional Fee-For-Service among HIV-Infected Beneficiaries of Medicaid?David Zingmond, MD, PhD

UCLA Division of General Internal Medicine and Health Services Research

June 8, 2004

background 1
Background (1)
  • Medicaid is the largest payer of healthcare for HIV/AIDS
    • Annual budget > $4.1B for HIV/AIDS
  • High costs of treating HIV/AIDS (and other diseases) have led to the adoption of managed care (HMO) in place of traditional fee-for-service (FFS) by Medicaid
  • Concerns that HMO enrollment might worsen care & outcomes of HIV/AIDS patients
background 2
Background (2)
  • In California, Medicaid HMOs and enrollment policy are implemented on a county-by-county basis
  • Depending upon the county, Medicaid managed care is mandatory, voluntary, or not offered.
hypotheses
Hypotheses
  • HMO enrollment is associated with lower hospitalization rates.
  • Medi-Cal HMO enrollment is associated with lower antiretroviral medication usage.
  • HMO enrollment reduces survival.
slide5

Conceptual Model

DISEASE STAGE

COMORBID DISEASE

DEMOGRAPHICS

  • ANTIRETROVIRAL THERAPY
  • HOSPITALIZATION
  • DISEASE PROGRESSION
  • MORTALITY

MEDI-CAL HMO ENROLLMENT

COUNTY POLICY FOR MEDI-CAL HMO ENROLLMENT OF HIV/AIDS PATIENTS

methods data sources
Data Source

Medi-Cal Eligibility File

Medi-Cal Claims

OSHPD Discharge File

Death Stat’l Master File

AIDS Registry & HIV Reporting System

Data Measures

Demographics & Enrollment

Antiretroviral Medication Usage

Hospitalizations, SCAH

Time to Death

Exposure Risk, CD4, Time since AIDS diagnosis

Methods: Data Sources

SCAH - Severity Classification of AIDS Hospitalizations

methods cohort definition
Methods: Cohort Definition
  • Identified all adult HIV/AIDS patients enrolled in Medi-Cal in January 1999 (in counties with mandatory or optional HMO enrollment) who were continuously enrolled until 12/2001 or death.
  • In sensitivity analyses, we relaxed restrictions regarding county of residence and of continuous enrollment.
methods dependent variables
Methods: Dependent Variables
  • Mortality by follow-up
  • Disease progression by follow-up
  • Hospitalization (or death) by follow-up
  • Use of HAART (at study baseline)

HAART - Highly Active Antiretroviral Therapy

methods independent variables
Methods: Independent Variables
  • Baseline HMO enrollment (& home county)

Covariates:

  • Demographics - Age, gender, & race
  • Comorbidity - non-HIV hospitalizations
  • Disease severity - HIV hospitalizations, CD4*, & SCAH*
  • Health Habits - Exposure risk category*
  • Treatment - Baseline HAART or ARV

* Only AIDS patient analyses

methods regression analyses 1
Bivariate comparison of dependent and independent variables by HMO enrollment

We employed standard multivariate probit regression model predicting:

Dependent Variable= Function(HMO Enrollment, Demographics, Disease Severity, Comorbidity, Treatment)

However, this approach may result in biased estimates if unmeasured severity is correlated with enrollment and outcomes.

Methods: Regression Analyses (1)
methods regression analyses 2
Solution - Treatment Selection Model (bivariate probit):

HMO Enrollment= Function(County Plan Type, Demographics, Disease Severity/Stage, Comorbidity, Treatment) + 

Dependent Variable= Function(HMO Enrollment, Demographics, Disease Severity/Stage, Comorbidity, Treatment) + 

The error terms of the two equations,  and , are modeled as being correlated.

Methods: Regression Analyses (2)
results demographics
Results: Demographics

** P < 0.01, * P< 0.05

results impact of hmo enrollment on aids patients
Results: Impact of HMO Enrollment on AIDS Patients

Covariates: age, race, gender, baseline HAART, baseline other ARV,

prior hiv- hospitalization, prior non-hiv- hospitalization, lowest CD4,

exposure category, SCAH.

P* - Chi-square test of rho coefficient different from 0

RR- Relative Risk with 95% CI calculated by bootstrapping with

1000 repetitions.

results impact of hmo enrollment on hiv patients
Results: Impact of HMO Enrollment on HIV+ Patients

Covariates: age, race, gender, baseline HAART, baseline other ARV,

prior hiv- hospitalization, prior non-hiv-hospitalization

P* - Chi-square test of rho coefficient different from 0

RR- Relative Risk with 95% CI calculated by bootstrapping with

1000 repetitions.

discussion 1
Discussion (1)
  • HMO enrollment in California appears to have negligible impact on hospitalization and death.
  • Despite concerns that HMOs might provide less necessary medications for AIDS patients, analysis results show no difference.
  • Important treatment guarantees may mediate the effects of plan type on outcomes
    • Include guaranteed access to medications and specialist providers
discussion 2
Discussion (2)
  • Differences in treatment appear to exist among the HIV+, non-AIDS patients.
    • Treatment criteria are less stringent for non-AIDS patients.
    • Disease severity is more varied but less well measured as that for AIDS patients.
  • Overall, the bivariate probit approach gives greater confidence to standard regression results.
limitations
Limitations
  • Single state
  • Limited follow-up
  • No ambulatory care data
  • HIV+ without AIDS patients had fewer case-mix measures
  • HMO implementation is heterogeneous and distributed geographically
conclusions and policy implications
Conclusions and Policy Implications
  • Medicaid HMOs for patients with HIV/AIDS have similar outcomes as standard FFS Medicaid.
    • Expansion of Medicaid HMOs may be justified if cost beneficial
  • Similar approaches may be used to examine benefits of managed care models for other medically needy Medicaid populations.